hx_gabor — 2D `halcon_ext` op

• Data kinds: image → image

• Call: fullseye.apply(img, "hx_gabor", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

• HALCON equivalent: convol_gabor (the HALCON reference is a useful guide to its meaning and parameters)

Usage

A Gabor filter (orientation theta = a*pi, frequency freq = 0.08 + 0.35b). Returns the magnitude of the response.

> The detailed description below is the original text — the summary and the headings are translated.

向きの規約は core の `gabor と同じ: a=0 (θ=0) が **縦縞**、a=0.5`

(θ=90°) が横縞に最も応答する。

★正規化(2026-09-02 の修正): カーネルの L1 ノルムで割る固定スケール。

以前は `_norm01`(その画像の min–max を [0,1] へ引き伸ばす)だったため、

向きによる応答の大小が潰れるどころか 順序が逆転していた —— 実測

(96×96 の横縞、b=0.5): 横縞検出器 (a=0.5) の平均 0.34663 に対し、ほとんど

反応しないはずの縦縞検出器 (a=0) が 0.58434 と高く出ていた(弱い応答ほど

引き伸ばし率が大きいため)。`|v| <= 1 なら |v * g| <= sum|g|` なので

L1 で割れば [0,1] を保ったまま向き・画像を跨いで比較できる。

Detailed usage guide

• gallery2d_halcon_ext family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• gallery2d_halcon_ext — py -3.11 examples/gallery2d_halcon_ext.py

Ops the type connects to (they accept `image` as input)

identity · gaussian · mean_box · bilateral · unsharp · median · min_filter · max_filter

Same category (`halcon_ext`)

hx_gen_circle · hx_gen_ellipse · hx_gen_rectangle2 · hx_gen_checker_region · hx_gen_grid_region · hx_fit_surface1 · hx_fit_surface2 · hx_cooc_feature

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

hx_gabor — 2D halcon_ext op